Job Description
From our origins in iPhone keyboard input, the Input Experience NLP team has expanded our broad charter: enhancing the user experience with robust language understanding and personalized text composition, across all Apple platforms and languages. Generative AI is a transformative technology, and we are just beginning to harness its potential to help users digest information and express themselves more clearly. On our team, you will help build the future and shape its evolution. Our team is responsible for key Apple Intelligence portfolios such as personalized Writing Tools, Summarization (Mail, Messages, Notifications, etc.) , Found In, Smart Actions as well as the entire keyboard backend: autocorrection, inline completions, proofreading, across all Apple platforms. Building on years of innovation in intelligent systems and on-device machine learning, we are now scaling efforts in bringing powerful foundation models (on-device and server) directly into everyday workflows.
We are looking for an engineering manager who can work at the intersection of ML, NLP and software engineering, specifically focused on innovating and evolving our data, tooling, modeling and evaluation pipelines with agentic harnesses, to scale globally. We are shifting the entire paradigm of ML product development across feature definition, data synthesis, model training, auto-evaluation, model probing, evaluation and user feedback to agentic workflows. You will have the opportunity to define and execute state-of-the-art paradigm for a swathe of high-impact features and languages, creating ML playbooks for scale, and influencing the rest of Apple. You will also be responsible for building and refining the personalized agent trajectory pipelines for data and evaluation across synthetic personas and languages. The role provides an opportunity to join an ambitious, collaborative team in a unique position to bridge the gap between cutting-edge ML research and features used by millions. You will work closely with cross-functional partners in human interfaces, user studies, internationalization, and system integration. You are not just developing technology; you are crafting experiences that feel like magic to the end user.
As an engineering manager, you will enable the next-generation of agentic ML product development systems at scale using Apple Foundation Models. You will sit at the intersection of cutting-edge research and product reality, bridging the gap between raw model performance and the nuanced needs of Apple customers worldwide. You will explore, design, and implement emerging techniques, ensuring alignment with product goals, privacy requirements, and performance metrics in a hands-on role. The role requires technical depth across ML & NLP, with a solid understanding of building scalable ML pipelines. You will redefine the ML development process across features and languages: problem formulation, experimentation, evaluation, fine-tuning, and continuous improvement that expand both the depth of Apple Intelligence’s capabilities and the breadth of its support for our global customer base.
Preferred Qualifications
Experience with deploying large ML models for real world products and leading high-performing teams
Experience curating, filtering, and synthesizing high-quality training datasets at scale
Familiarity with ML pipelines that need to scale across languages
Experience developing and training models for agentic workflows, tool calling and advanced reasoning techniques
Familiarity with working and managing complex, large-scale codebases, with a strong emphasis on writing high-quality, maintainable, and well-tested code.
Experience using AI-assisted development tools (e.g., Claude, Copilot, or similar) to accelerate experimentation, code development, and research workflows
Minimum Qualifications
Masters or PhD in Computer Science, Electrical Engineering, Physics, Statistics or related field; or equivalent practical experience
Prior experience with technical leadership or engineering management
Strong foundation in Data Science and MLOps
Familiarity with product ML/NLP lifecycle
Familiarity with techniques such as SFT, RLHF, Data Synthesis, Parameter-Efficient Fine-Tuning, LLM-judge evaluation
Excellent communication skills
